Existing merchandise marketing websites often use keywords recently searched by a user to recommend sellers to the user. For example, based on the keywords entered into a search engine to look up matching sellers, the websites will use those keywords to recommend sellers to the user.
The above recommendation method is relatively simple, which can make it difficult for the recommendation method to satisfy the goals of sellers, buyers and operators. For example, often the most popular products are recommended to buyers, or sellers that a buyer is already familiar with are recommended more frequently than other sellers because when the buyer and the seller have already had previous contact, a higher probability exists that another transaction will occur than for recommendations where the buyer and the seller have had no previous contact.
In addition, with the recommendation method described above, keywords cannot be used if the user has not conducted any searches recently. In order to provide seller recommendations to as many users as possible, a large volume of search data over a long period of time needs to be available. The large volume of search data requires the usage of a large amount of system memory.
Furthermore, even if the buyer has recently searched related content, providing seller recommendations to the user while the user is browsing, completing a transaction or bookmarking a specific product, involves extracting keywords and performing searches extemporaneously. During extemporaneous extraction of keywords for searching, the extraction of the keywords requires time, and also performing search operations using the extracted keywords require an analysis of all of the user's behavior data. Accordingly, the extracting and the performing operations require a very large computational load, have low computational efficiency, and the computed results tend to have low accuracy.